Novel machine learning techniques based hybrid models (LR-KNN-ANN and SVM) in prediction of dental fluorosis in groundwater

Atas, Musa; Yeşilnacar, Mehmet İ̇rfan; Yetiş, Ayşegül Demir

doi:10.1007/s10653-021-01148-x

Cited by 10 publications

(2 citation statements)

References 52 publications

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“…Ngnamsie Njimbouom et al developed a decision support system based on machine learning algorithms to assist in treatment planning for dental caries [8]. A similar study of the use of AI was also reported on the regarding the detection of fluoride concentration in drinking water in Turkey, and the results showed that the use of AI was cheaper, faster, and more feasible than the use of many chemical analysis techniques available in the laboratory [20].…”

Section: Discussionmentioning

confidence: 99%

Influence of Artificial Intelligence-Driven Diagnostic Tools on Treatment Decision-Making in Early Childhood Caries: A Systematic Review of Accuracy and Clinical Outcomes

Al-Namankany

2023

Dentistry Journal

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Early detection and accurate prediction of the risk of early childhood caries (ECC) are essential for effective prevention and management. This systematic review aims to assess the performance and applicability of machine learning algorithms in ECC prediction and detection. A comprehensive search was conducted to identify studies utilizing machine learning algorithms to predict or detect ECC. The included (n = 6) studies demonstrated high accuracy, sensitivity, specificity, and area under the receiver operating characteristic (AUC) values related to predicting and detecting ECC. The application of machine learning algorithms contributed to enhanced clinical decision-making, targeted preventive measures, and improved ECC management. The studies also highlighted the importance of considering multiple factors, including demographic, environmental, and genetic factors, when developing dental caries prediction models. Machine learning algorithms hold significant potential for ECC prediction and detection, having promising performance outcomes. Due to the heterogeneity of the studies, no meta-analysis could be performed. Moreover, further research is needed to explore the feasibility, acceptability, and effectiveness of integrating these algorithms into dental practice. This approach would ultimately contribute to enabling more effective and personalized dental caries management and improved oral health outcomes for diverse populations.

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Section: Discussionmentioning

confidence: 99%

Influence of Artificial Intelligence-Driven Diagnostic Tools on Treatment Decision-Making in Early Childhood Caries: A Systematic Review of Accuracy and Clinical Outcomes

Al-Namankany

2023

Dentistry Journal

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“…Yaqub et al 21 developed ML models to predict nutrient removal from wastewater treatment. Atas et al 22 expanded hybrid models such as linear regression, ANN, K‐nearest neighbor, and support vector machines to predict the fluoride amounts from wastewater and achieved the correlation values of 0.731. Sudhakar et al 23 Proposed a DL algorithm and validated it against three other ML models, Random Forest, eXtreme gradient boosting, and ANN, to evaluate groundwater quality.…”

Section: Introductionmentioning

confidence: 99%

Modeling the adsorption process for fluoride removal from groundwater by machine learning

Reddy,

Maurya,

Hyeon‐A

et al. 2023

Env Prog and Sustain Energy

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Worldwide, groundwater pollution with heavy metals is a severe concern, threatening living organisms and drinking water safety. High fluoride concentration is a common pollutant among various heavy metals found in groundwater. The adsorption method was more convenient, efficient, economically feasible, and eco‐friendly for removing the excess fluoride from groundwater. The fluoride removal efficiency depends on the adsorption process variables such as contact time, pH, alumina dose, temperature, and agitation speed. The association between fluoride removal and adsorption process variables is complex and non‐linear. The present study developed an artificial neural networks (ANN) model to calculate the effect and analyze the relationship between adsorption process variables and fluoride removal. The ANN model was trained using the backpropagation algorithm. The estimated fluoride removal was in good agreement with the experimental observations, with an accuracy of (R2 >99.6) for both training and testing datasets, and was superior to the existing models. The accurate predictions exposed that the model could adequately estimate the relationships between adsorption process variables and fluoride removal from groundwater.

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Comparison of deep convolution and least squares GANs for diabetic retinopathy image synthesis

Ataş

2023

Neural Comput & Applic

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Novel machine learning techniques based hybrid models (LR-KNN-ANN and SVM) in prediction of dental fluorosis in groundwater

Cited by 10 publications

References 52 publications

Influence of Artificial Intelligence-Driven Diagnostic Tools on Treatment Decision-Making in Early Childhood Caries: A Systematic Review of Accuracy and Clinical Outcomes

Influence of Artificial Intelligence-Driven Diagnostic Tools on Treatment Decision-Making in Early Childhood Caries: A Systematic Review of Accuracy and Clinical Outcomes

Modeling the adsorption process for fluoride removal from groundwater by machine learning

Comparison of deep convolution and least squares GANs for diabetic retinopathy image synthesis

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